Interpretation - Science topic

Today I tested a robot I designed a while ago to simulate human emotions and make a decision, but I see that the decision will not be in the best interest of humans. How will we analyze that?

Considering that the items comprising the Environmental Identity scale encompass aspects related to environmental identity, such as enjoyment of nature, appreciation of nature, and environmentalism, how does the interpretation of the construct differ depending on whether a second-order confirmatory factor analysis model or a bifactor model is obtained?

Thank you for the attention.

Best regards,

Ana

under likert-scale questionaire

Never

Rarely

Sometimes

Often

Always

Can we add the results of Never and Rarely=?

Often+Always=? for the purpose of data interpretation purpose?

I have a question about reporting Pearson Correlations. I have a total scale (EBPAS) with four subscales. I would like to report how the total scale and each subscale correlate with the independent variables. Is the following sentence a correct interpretation of the results:

The 3-point scale measuring "interest in research" correlated significantly with the EBPAS total scale (r = 0.31, p = .001). In particular the subscales' openness (r= .203, p= .024) and divergence (r= -.232, p= .012) significantly correlated with the EBPAS, whereas the subscales attitude and appeal did not show significant correlation with interest.

The results indicate that out of the EBPAS openness and divergence towards EBP have the strongest explanatory power for interest in research.

Hello! I am currently completing my undergraduate thesis, which adapts the VIA. It uses a 9-point Likert scale, and I am trying to clarify the scale range and verbal interpretations associated with it. I know that the VIA assesses two dimensions, but I would appreciate it if anyone could provide more detailed information about the scale’s scoring system and its interpretation, or any relevant references to help with my analysis. In multiple sources, the only information given are similar to this: https://www.carepatron.com/templates/vancouver-index-of-acculturation

I have e-mailed the author of the scale, but decided to inquire here for good measures. Thanks in advance!

I am currently exploring research areas related to interpretable and explainable AI, with a particular interest in model-agnostic approaches. I would greatly appreciate your valuable suggestions in this field.

How should one interpret shifts in endothermic and exothermic DTA peaks of a semicrystalline material after polymer coating if the shifts occur towards lower temperatures?

I am studying metapelitic rocks in contact with a granitoid. I am trying to use perplex to create pseudosections of my samples but I need help/guidance for the interpretation of the results. Any help or suggestion will be helpful

Body language and tone of voice augment actual words

Speech acts, conversational maxims of Grice, implicature

Pragmatics

Pragmatics is a subfield of linguistics that studies how context influences the interpretation of meaning in language. It goes beyond the literal meaning of words to understand how language is used in real-life situations.

Speech Acts

Speech acts are communicative actions performed through language, such as making statements, asking questions, giving commands, making promises, and more. J.L. Austin and John Searle are two prominent figures in this theory. Speech acts can be categorized into:

- Locutionary Acts: The act of producing sounds and words.

- Illocutionary Acts: The intention behind the utterance (e.g., requesting, promising).

- Perlocutionary Acts: The effect on the listener (e.g., persuading, frightening).

Grice's Conversational Maxims

H.P. Grice proposed four conversational maxims that guide effective communication:

1. Maxim of Quantity: Provide the right amount of information—not too little, not too much.

2. Maxim of Quality: Be truthful and do not provide false or misleading information.

3. Maxim of Relation: Be relevant and stay on topic.

4. Maxim of Manner: Be clear, brief, and orderly; avoid ambiguity.

Implicature

Implicature refers to what is suggested in an utterance, even if not explicitly stated. Grice introduced the concept to explain how listeners can infer additional meaning based on the context and the conversational maxims. There are two main types:

- Conventional Implicature: Meaning that is tied to specific words or phrases (e.g., "but" implies contrast).

- Conversational Implicature: Meaning derived from context and conversational principles (e.g., inferring "there is no milk" from "the store is closed").

These concepts help us understand the intricate ways in which meaning is constructed and interpreted in communication.

In an attempt to predict a binary outcome (yes/no), one continuous predictor variable of my 10 predictor variables (both continuous and categorical) is showing non-significant p-value in univariate but significant p-value in multivariate analysis. How to interpret and explain this? In the multivariate analysis, there are 5 predictor variables showing significant p-value.

Hello! I'm new to using AFM to analyze nanoparticles in a solution, and I have a question. I see a "line fit 2.79 nm" bar on the right side of my image. Does this refer to the height of the features in the image? For example, does the white area correspond to an approximate height of 2.79 nm? I noticed in another sample, which doesn't have nanoparticles, that it shows "line fit 700 pm." From this, I’m guessing that this value might represent the maximum height. Could you confirm this? Thank you!

I've test TGA and plotted that, but the result of weight loss didnt start from 100, why this is happen? And how can i compare this with other variable??

I have LCMS test data in my hand. I need help how to analysis what compounds/substances are there (The material is crude plant extract). I'm attaching the full chromatogram for reference. It would be very helpful if I could be pointed to some resources for making sense of this result.

How to interpret the SPSS output of parallel mediation analysis Hayes PROCESS Model no.4? i have one independent variable and one dependent variable and two mediating variables in my research.Experts and researchers , please guide me in this regard, which things in the output i have to interpret???

The term "good neighbourliness" is used in the 1991 Minsk Agreement on the establishment of the CIS. How would you interpret this statement? If a neighbor takes actions that directly or indirectly harm its neighbor, is the principle and duty of "good neighbourliness" violated?

Термин «добрососедство» используется в Минском соглашении 1991 года о создании СНГ. Как бы вы интерпретировали это заявление? Если сосед предпринимает действия, которые прямо или косвенно вредят его соседу, нарушается ли принцип и обязанность «добрососедства»?

I need interpretation of following attachments

Dear Professor

I am a PhD student in tourism and marketing at the University of Abu Bakr Belkaid, currently working on my dissertation titled "The Role of Marketing Innovation in Influencing Tourists' Behavioral Intentions." My study aims to incorporate the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) as theoretical frameworks.

I am reaching out to request comprehensive guidance throughout the entire research process, from the initial design to the final stages of analysis and interpretation. Specifically, I would need support with:

Your expertise in this field would be invaluable to me, and I would be truly honored to receive your support at every step of the process. I fully understand your time constraints and will gladly accommodate your schedule.

Thank you very much for your consideration. I look forward to your response.

Kind regards, Ben Senane Ahmed Abdou Toaub Email: touwebbenssenane@gmail.com

Hi everybody,

I had originally created a box plot (which I will attach down below) which showed highly skewed data. I read that one of the options to make it easier to interpret was to perform log transformation of the Y axis, which I did on R with scale_y_log10().

On the figure in which I had not performed log transformation yet, for the value of 4 (X-axis) the median was either 0 or very close to 0 and there was no lower quartile; but when I performed the log transformation, it shows that the median is actually somewhere in between 6 and 7.

My question is, if the median was 6/7 all along, shouldn't that have shown in the first graph since it is not a really low value? Does it mean that my interpretation of the box plot changes because I performed log transformation, or do I interpret it the exact same way as I would have interpreted the original one?

Thank you!

support me to fine anwser for my project

My question is about the papers those take logarithm to reduce the skewness of the compositional data like elements concentration and use common statistical methods to analyze the data in log scale and their results seems reasonable to them and put some interpretation on their results and publish it.

My question is:

Regarding ALR, CLR, and ILR transformations, is it really a MUST to transform compositional data by, say, CLR or ILR transformations before applying statistical methods to the data such as factor analysis?

Thanks

I propose a discussion on my text "Notes on Amartya Sen’s interpretation of cultural identity". The text has been published in Progetto Montecristo – Editoriale Delfino, 2024 (Part 1, 17th October 2024; Part 2, 13th October 2024; Part 3, 5th November 2024). My version of the text is available at the bottom of this announcement as an attachment. The printed text can be read at the following web addresses: https://progettomontecristo.editorialedelfino.it/notes-on-amartya-sens-interpretation-of-cultural-identity-part-1/?fbclid=IwY2xjawF-LO5leHRuA2FlbQIxMQABHcksJSIA5mmlR36zzHgGEDR7CF3t3zBmlVl7hcfm4DSXQKZN0fK_Z6Ck7A_aem_UUlZA9crjYqCO-rI22wBBA https://progettomontecristo.editorialedelfino.it/notes-on-amartya-sens-interpretation-of-cultural-identity-part-2/?fbclid=IwY2xjawGF_i1leHRuA2FlbQIxMQABHRV3C-JbUiuvxiKFWvr0HAjR1y4g5zQFFR4Y8eRS4UZ2W-3HF0ooC7WLcA_aem_BNrERzoP9mu6XDskwUz63A https://progettomontecristo.editorialedelfino.it/notes-on-amartya-sens-interpretation-of-cultural-identity-part-3/?fbclid=IwY2xjawGWrLFleHRuA2FlbQIxMAABHbXCqP7QOzBkC1mXRe1du63cQqqI1C54Miq4yKUonC_S4Znq6ilgK-0z8w_aem_JBI6HiMQHbA6_Zci1IM0rw In our study, we analyse aspects of Sen’s criticism of specific interpretations of cultural identity. We shall see that, in Sen’s view, different interpretations of cultural identity can be given. The different ways in which the concept of cultural identity is interpreted correspond to different ways of living one’s culture; they are connected to different interpretations of religion and religious identity too. Throughout Sen’s inquiry, we shall find the following interpretations of cultural identity: - The first interpretation of cultural identity, which corresponds to Sen’s interpretation of cultural identity, considers cultural identities as the results of a plurality of components which constantly evolve (this might be defined as the flexible, dynamic, and inclusive view of identity). - The second interpretation considers identity as rigid, complete, isolated, and given once and for all (this could be defined as the rigid and static conception of identity). The second conception of identity corresponds to the aim of producing people and groups as isolated systems. Sen investigates the psychological mechanisms connected to the rigid interpretation of cultural identity. Individuals can be manipulated through the rigid interpretation of identity. Sen shows that the rigid interpretation of cultural identities can be used to marginalise all those who do not belong to those same cultural identities. This interpretation of the cultural identity aims to divide individuals, groups, peoples, and nations from each other. Cultural identities can be used to create a group which, as such, does not exist at all or is not so homogeneous and uniform as those who support this concept of identity aim to let appear. The group is created artificially by an artificial cultural identity. The rigid cultural identity of some groups means the exclusion of other groups. This kind of cultural identity serves to bring about enmity between individuals, groups, nations, countries, and communities: it is thought out to produce hostility from a group towards other groups. In Sen’s view, cultural identities always result from a plurality of cultural components. Cultural identities take elements from other cultural identities. Therefore, cultural identities are not isolated systems: they are the product of a historical development which involves the participation of different individuals, groups, and cultures. Moreover, cultural identities are not made once and for all: on the contrary, cultural identities are dynamic phenomena which continuously take in new elements. For our investigation, we shall refer to Amartya Sen’s study "Identity and Violence. The Illusion of Destiny".

In my scholar work, the consistency was supported by training team members in coding techniques and using standard codes across datasets. Regular meetings were conducted to align on coding interpretations also to help maintain uniformity across different data sources (Saldaña, 2016).

This experience makes it clear that bringing in objectivity in qualitative data can be quite challenging, but this experience was worth it to learn the same indeed.

What it is, does children’s literature help readers to interpret, understand, and define human life? Please share your views and suggestions.

the College Academic Perfectionism Scale (CAPS). We are currently utilizing in our research with 127 respondents.

We have reviewed the test manual and appreciate the valuable information provided. However, we were unable to locate specific guidance on interpreting scores for the individual domains (SOP, SWC, COM, DAA, SC, and SPP).

To address this gap, we are exploring different approaches for assigning interpretations (Low, Average, High) to our domain scores:

We would greatly appreciate your insights on the most appropriate method for interpreting domain scores within our study.

Thank you for your time and consideration. We look forward to your response.

I'm preparing the research about interpretation of automation, digitalization and digital transformation. I'm interested in different points of view in this question

One key limitation is multicollinearity, which affects the interpretability of results. Moreover, oversaturation in models with too many predictors can result in overfitting. Small datasets, or sparse data, can also challenge the accuracy of logistic regression models.

Hello,

I'm planning a shell exchanging experiment with two marine, hermit crab species inside of a tank. I only have one tank available for this experiment. I plan on running 30-40 shell exchanging trials, each trial lasting 48 hours. During each trial, I will place 2 individual hermit crabs, one of each species, inside a tank with a single empty shell. Note, both hermit crabs will be wearing damaged shells. The objective of this experiment is to see if one of the species exhibits a higher frequency of taking the empty whole shell than the other, which we would interpret as one species being more dominant. The idea is that each trial will be conducted with new individuals and new shells.

My questions are, is the Chi-Square test the appropriate statistic for this hypothesis? Lastly, if it is, could someone give me an example of what the contingency table/matrix would like for the analysis?

Many thanks,

Miguel

In the realm of physics, the relationship between quantum mechanics and thermodynamics has long posed a significant challenge. The Many-Worlds Interpretation (MWI) offers a fresh perspective, allowing us to rethink the implications of quantum events and their potential connections to entropy.

1. Fundamentals of Many-Worlds Interpretation

According to the Many-Worlds Interpretation, when a quantum event occurs, the universe splits into multiple parallel universes, each corresponding to a possible outcome. This viewpoint emphasizes the diversity and uncertainty inherent in quantum phenomena, challenging the classical understanding of measurement and observation.

2. Defining Entropy and Its Increase

Entropy is a physical quantity used to measure the disorder of a system. According to the second law of thermodynamics, in a closed system, entropy will naturally increase over time. The growth of entropy signifies the randomization of energy distribution and the reduction of usable energy within the system.

3. Analogy Between Many-Worlds Interpretation and Entropy

If we regard the "multiverse" as a closed system, the emergence of new universes with each quantum event can be seen as an increase in the states of the system. This point bears a certain similarity to the growth of entropy, as each universe split represents the addition of new possibilities and outcomes, thereby enhancing the overall disorder of the multiverse.

4. Impact of Quantum Events on Entropy

The occurrence of quantum events, especially in interaction with the external environment, leads to the phenomenon of decoherence, whereby quantum states become classical and more disordered. This process is closely related to the concept of increasing entropy, as the complexity and uncertainty of the system rise with the occurrence of quantum events.

5. Reconsidering the Physical Framework

Incorporating the Many-Worlds Interpretation into the discussion of entropy prompts us to rethink the boundaries between quantum physics and thermodynamics. In a sense, this line of thinking breaks the traditional physical framework, enabling us to find new relationships on both macroscopic and microscopic levels.

Conclusion

The exploration of the intersection between quantum mechanics and thermodynamics remains a promising area in contemporary physics research. The relationship between Many-Worlds Interpretation and entropy offers a new way of thinking that fosters a deeper understanding of the nature of the universe. As scientific technology continues to advance, these discussions may inspire further inquiries into the principles governing the workings of the universe and potentially lead to breakthroughs in our understanding of physics.

Truth has been defined by many thinkers, writers, philosophers, and individuals from various backgrounds. Each interpretation contributes to the definition of truth in its own way, sometimes clarifying the concept and, at other times, complicating it. Over the centuries, the essence of truth has been explored in existential, relativistic, absolutist, cross-cultural, and other contexts, enriching the intellectual depth of societies. I would like to discuss the nature of truth and its theoretical and practical aspects: How do you define truth based on your perspective?

Lorentz transforms provide a means to predict what another observer (if diff reference frame) will see or experience for a given shared in their timeline event, i.e the time Flow in the reference, the spped of object related to event etc, between any observers. This is surprisingly and excitibg that a single, unified blueprint can do this job, and it took 3 centuries after Newton's to be discovered. It is a law of nature, although its called transform.

Part of the reason is that the prevaili g interpretation of it is Special Relativity and the spacetime construct and that there is no other.

Few scientists, such as Saha claimed that SR is just one and possible idiosyngratic interpretation of this "blueprint" and that it wrongly has been establishd as the equivalent of it.

I conducted a multiple linear regression using gretl for my exploratory research. The data was from published reports from 2013 to 2021, as these were the periods where all the yearly data was recorded. The results show that two of my variables are significant at 10% and have an r-square value of 0.664428, but the overall model fit comes to F(3,5) = 3.299978 and p-value (F) = 0.115750. So, can I go forward with this result or not and how can I justify this result?

I have calculated Cohen's d effect sizes in SPSS by running the test 'One-sample t-Test' of the 'difference scores' i.e., t1 - t2 . How valid and reliable these effect sizes are? And what is the best interpretation of these values?

I made purple macckonecky agar media but I couldn't interpret the results as some plates show no growth, some plates color change to yellow and some show growth with no change in coloration

How can we effectively balance the trade-off between model interpretability and predictive performance in complex machine learning systems, especially in high-stakes domains like healthcare or criminal justice?

Balancing interpretability and predictive performance in high-stakes machine learning domains, such as healthcare and criminal justice, is a multifaceted challenge that requires careful consideration of various factors. In these fields, decisions based on machine learning models can have profound impacts on individuals' lives, making transparency crucial.

To address this balance, it is essential to adopt a tiered approach. First, one can utilize simpler, inherently interpretable models, such as logistic regression or decision trees, when possible. These models offer clear insights into how input features influence outcomes, making it easier for stakeholders to understand and trust the decision-making process. However, these models may not always achieve the same level of predictive performance as more complex models like deep learning or ensemble methods.

When complex models are necessary to improve accuracy, techniques such as model-agnostic interpretability methods (e.g., SHAP, LIME) can be employed. These methods help elucidate how different features contribute to predictions without compromising the underlying model's complexity. By providing post-hoc explanations, these techniques enable practitioners to retain high performance while gaining some level of insight into the model's behavior.

Additionally, involving domain experts in the model development process can enhance interpretability. Their input can guide feature selection and highlight the importance of specific variables, ensuring that the model aligns with domain knowledge and ethical considerations. Furthermore, using visualizations to illustrate model behavior can aid in communicating results to non-technical stakeholders, fostering a better understanding of the decision-making process.

It is also vital to establish clear ethical guidelines and standards for model use in high-stakes environments. Implementing rigorous validation and testing procedures, along with continuous monitoring of model performance and biases, helps ensure that the models remain reliable and justifiable over time.

Lastly, organizations can promote a culture of transparency by encouraging open discussions about the limitations and risks of machine learning applications. This includes acknowledging when a model's predictive performance may come at the expense of interpretability and vice versa. Ultimately, the goal is to strike a pragmatic balance that prioritizes ethical considerations, user trust, and the potential societal impact of machine learning technologies while leveraging their strengths in predictive capabilities.

I am currently working on multilingual proceedings and the role of the interpreter in criminal trials in France. There are certain problems that are intrinsic to language, the act of translation/interpretation and the demands and constraints of the legal setting. To make sure that the rights of non-native speakers are protected in interpreter assisted trials, some of the solutions proposed are preservation of the original, specialised training for legal professionals on working with interpreters and legal training for interpreters.

Would you agree with these solutions and/or want to add to them? Also, one of he counter-arguments is often over-burdening of the courts and the tendency to dismiss certain solutions as unpractical. Do you agree?

How should i interpret the BET datas if the surface area and pore volume increases when the metal is doped on support material while pore size decreases (from mesoporous to microporous)? In addition, after the use of catalyst, all of surface area, pore volume and pore size decrease. Do you have any article recommendations about interpreting the BET results about metal-doped catalysts for gaining wider knowledge? It is harder as a chemical engineering student to interpret the characterization datas since no lectures are given about it. I'll appreciate your answers!! Kind regards.

Hello everyone,

I am currently working on a molecular docking project and have obtained results that I need help interpreting. I would greatly appreciate any guidance on how to analyze these results effectively, including insights about RMSD (Root Mean Square Deviation), binding affinities, binding poses, hydrogen bonds, and the amino acids involved in these interactions.

Any resources, tips, or personal experiences you could share would be immensely helpful!

Thank you in advance for your assistance!

The question is how to interpret objectively the intensity of color change in the enzymatic reaction of the test. Is there any control color scale available, since the positive reaction is graded based on color intensity (1,2,3,4,5)? It seems too subjective without a control scale.

This is from dielectric spectroscopy data for a metal-oxide ceramic that has two major phases and a small amount of a third phase present. It's a plot of AC conductivity vs angular frequency for this sample at a particular temperature.

I have tried both the single and double power law's. I've tried adding a third and fourth term. How do I interpret this result? What should I use to fit this curve properly?

EDIT: I am attaching images of the curve in question. In one image, the plot was changed to a log-log plot.

How to perform an interpretative structural modelling?

Good evening, can you help me interpret the results of the 1% agarose gel.

DNA was extracted from plant tissue using the CTAB protocol. In the wells (left to right) in well 1 and 4 is the molecular weight marker (1kb) with the amount of 1μl, well 2 has 1μl of sample + 1μl of loading buffer. well 3 μl of sample + 1μl of loading buffer.

Electrophoresis conditions: 80V x 30 min.

I am mainly interested in understanding the banding pattern of well 2.

Thank you.

In human psychology, time is a conscious experience—a construct reflecting the sequence of existence and events. In cosmology and physical sciences, time is often defined as the indefinite, continuous progression of existence and events in a uniform and irreversible succession, extending from the past, through the present, and into the future. This progression is conceptualized as a fourth dimension that exists above the three spatial dimensions.

Time is fundamentally a measurement to quantify changes in material reality. The SI unit of time, the second, is defined by measuring the electronic transition frequency of caesium atoms. Time is also recognized as one of the seven fundamental physical quantities in both the International System of Units (SI) and the International System of Quantities.

In physics, time is commonly defined by its measurement—essentially, "what a clock reads."

This description suggests that time, in its conventional understanding across various scientific disciplines and human experience, is an abstract concept, not a real, tangible entity. While time provides a framework for understanding the succession of events, it does not have a direct physical existence as space does in three dimensions. Time is often viewed as a hyper-dimensional abstraction—imperceptible and unreachable beyond the three-dimensional spatial realm.

However, relativity challenges this interpretation by treating time as a real entity—integrated with space to form a four-dimensional space-time continuum where time becomes subject to physical modifications, such as time dilation. This relativistic concept implies that time is not only concrete but also malleable under the influence of velocity and gravity, leading to discrepancies with other scientific interpretations that consider time an abstract or imaginary concept.

One of the main contentions is that time dilation, a cornerstone of relativity, effectively violates the standardization of time by presenting it as something dilatable, thereby questioning the uniformity and constancy of time itself. The traditional time scale based on a 360-degree cycle—representing a consistent progression—is disrupted by the relativistic notion of time dilation, which converts abstract time into something perceived as "real" or "natural." This treatment of time also seems to ignore the conscious human experience, which understands time as a subjective, psychological construct.

Furthermore, if time is not directly reachable—being an abstract hyper-dimensional concept—what then is the "time" that a clock measures? Clocks are designed to provide a standardized approximation of cosmic time through calibrated frequency counts, such as the electronic transitions of caesium atoms. However, the physical manifestation of time in clocks is inherently subject to distortions, primarily due to gravitational effects. Gravity affects mass and energy, altering the oscillation rates of clocks and resulting in time distortions. Consequently, even the most accurate atomic clocks require periodic adjustments to compensate for these external influences.

The discrepancy between the "real time" measured by clocks and the "conceptual time" of cosmic progression raises further questions about the nature of time. Clocks, intended to represent a uniform progression of time, must contend with gravitational influences that disrupt this uniformity, necessitating ongoing corrections. This challenges the idea that time is a tangible, concrete entity and supports the view that it remains fundamentally an abstract concept—a conceptual framework through which we interpret the order of existence and events.

In short, while relativistic physics proposes that time is a real entity susceptible to physical modifications like time dilation, this interpretation remains contentious when viewed through the lens of broader scientific understanding. Time appears more consistent with an abstract or imaginary concept, a near-approximate representation that is susceptible to external influences, yet ultimately remains beyond the realm of tangible existence.

The Sydney School versus Berkeley...

We performed liquid phase NMR with deuterated DMSO as our solvent of a number of treated FAI samples. For all samples we observe a slight shift in the H2O signal (~3.36 ppm instead of the expected ~3.30 ppm), which we assume to be due to interactions between H2O and the FAI. However, one of the samples seems to present an asymmetric H2O peak (attached), which we interpret as a second peak overlapping our first. Our first assumption is that this is evidence of a new material that is interacting with our H2O separately from our main sample. Are there other reasonable assumptions that we could test for?

Our process performs shimming automatically and we have not had issues with shimming on any other measurement. The water signal in our samples should be due to water contamination of our DMSO as our sample should not contain water. We expect the sample to be mildly acidic.

To add additional context, FAI refers to Formamidinium iodide (CH5IN2)

Does ANOVA test correlation among variables? If not, how do you interpret the post hoc analysis generated by SPSS during ANOVA analysis?

I have been performing zymography and am having little difficulty in band interpretation.

my gels are not looking properly

what can i do to improve that?

and the analysis of the gel's band characterization.

How AI Can Be Made Explainable and Interpretable?

Hi, I have a set of hypotheses about main effects and a set of hypotheses about moderating effects. One of the main effects is insignificant (H rejected). Namely, Capability does not predict Fraud (in my data). However, the moderating hypothesis "Machiavellian personality moderates effect of Capability on Fraud" is significant. According to my calculation, the statistical power in my regression models should be good, therefore my findings reliable. Still, I am not sure how to interpret this situation. So MACH changes (negatively) the slope of CAP=>FRAUD. But there is no reliable slope! Operationalization of CAP and MACH is consistent with the literature; FRAUD is measured through a lab experiment. Any ideas on how to interpret this? Also, I had to run regressions with moderators without the main effects due to huge multicollinearity. So I have two sets of models, models with main effects (just IVs / IVs + controls) and models with moderators (just MODs / MODs + controls).

I have data from two replica experiments conducted at different times. Each has two factors (A and B). Factor A has 20 observations which was done in replicates of three. Factor B has 3 observations. I want to compare at how each observation in factor A differ within their respective experiment times per observation of factor B and also compare how the same observation in factor A compare to each other in the two experiment. I have taken two approaches:

1: NAME <- aov(response ~ FactorA * experiment time , data = data) (do a posthoc analyses with "FactorA * experiment time " in the formular)

2: NAME <- aov(response ~ FactorA , data = data) (do a posthoc analyses with only "FactorA" in the formular)

For the second option i also intent to one by one anova of the 20 observation from each experiment and then tabulate them.

I can attach two graphs of each if needed.

Please advise

Initially, I obtained poor results using only three training models. However, after applying cross-validation during training, the model's performance improved significantly. I would like to print the result for each k-fold, calculate and print the mean of these results for more detail, and finally print the results for the testing data

In my study, I am interested in measuring a complex construct using a validated multi-dimensional scale. However, due to constraints such as survey length and respondent burden, I am considering using only one dimension of the scale that aligns most closely with my research objectives. Is it methodologically sound to use just one dimension of a multi-dimensional scale to measure the construct, or would this compromise the validity and reliability of the findings? How might this decision affect the interpretation of the results and the overall quality of the study?

Why acceptance criterion for microbiological quality as below

10 ^1 cfu: maximum acceptable count = 20 not 10

10 ^2 cfu: maximum acceptable count = 200 not 100

10 ^3 cfu: maximum acceptable count = 2000 not 1000

How can I interpret the data gathered without solving?

Hello everyone! I am currently engaged in an osteogenesis research project involving Mesenchymal stem cells. Following a 15 and 16-day induction period, I would greatly appreciate any guidance or insights regarding the interpretation of these images. Thank you!

Hello community,

I am studyng the electrochemical behaviour of some series of materials based on graphene oxide chemically modified with aliphatic amines attaching palladium nanoparticles. I am not familiar with electrochemistry so I really need help to make a correct interpretation of the voltammograms obtained and what essencially the graphs mean. I would really appreciate your help and further comments and suggestions.

It's a carbon sample (powder form) derived from a biomass source after carbonization. No activation or post treatment. I know the peaks at ~25 and 43 correspond to C(002) and C(100) but the other four peaks particularly the peaks at 33.8 and 35.7 have got me stumped. Thanks

In discrete systems, such as Markov chains, Shannon entropy can be used to explain the uncertainty and complexity of the system. In continuous systems, such as pure jump Markov processes, does the corresponding differential entropy have a clear physical meaning? If so, how can differential entropy be used to interpret continuous systems?

i am unable to interpret why its increases in start as shown in figure

This discussion concerns the positivist versus the realist interpretation of quantum non-locality in the framework of EPRB experiment. It's about the possibility to change this question of interpretation into a falsifiable proposal: the conservation (or not) of 2-time correlations on Bob's side as long as only Alice performs polarization measurements.

More precisely, the article "Each moment of time a new universe" (https://arxiv.org/abs/1305.1615) by Aharonov, Popescu and Tollaksen, presents:

Cf. this very interesting 8 pages article (https://arxiv.org/pdf/1305.1615) and a video presented by Popescu (https://www.youtube.com/watch?v=V3pnZAacLwg).

Thanks to their 2-state vector T-symmetric formalism (https://arxiv.org/abs/quant-ph/0105101), Aharonov, Popescu and Tollaksen notably highlight the following facts:

Concerning EPRB type experiment, this document indicates §Measurements on EPR state:

Thus, the 2-state vector time-symmetric formalism shows the asymmetry of the quantum state obtained, during an EPRB experiment, after a measurement carried out on one side only. That asymmetry doesn't show up in the standard formulation. Consequently, the standard one-state vector time-asymmetric quantum formalism suggests a (hidden) relativistic causality violation. On the contrary, the conservation of the 2-time correlation in the 2-state vector formalism provides, in my view, a proof that, on Bob's side, nothing happens as long as only Alice carries out quantum measurements on her side.

This seems providing a testable prediction allowing us to decide between:

- Bohm, Bell https://web.archive.org/web/20190104202702/http:/www.tcm.phy.cam.ac.uk/~mdt26/PWT/lectures/bohm5.pdf

- Goldstein https://arxiv.org/abs/0903.2601

- Bricmont https://academiesciencesmoralesetpolitiques.fr/wp-content/uploads/2019/01/3-bricmont.pdf

- Scarani https://www.scielo.br/j/bjp/a/k9YqsvczjxzH8ybXg5YHHqH/?format=pdf&lang=en

- Valentini https://arxiv.org/abs/quant-ph/0112151

- Selleri https://www.researchgate.net/publication/225993103_Superluminal_Signals_and_the_Resolution_of_the_Causal_Paradox

- Percival https://arxiv.org/abs/quant-ph/9803044

- Towler https://casinoqmc.net/pilot_waves.html

- Hemmick https://arxiv.org/pdf/quant-ph/0412011

- Aspect https://tp.physique.usherbrooke.ca/experiences_fichiers/Bell/web/Aspect.pdf

- Special Relativity and possible Lorentz violations consistently coexist in Aristotle space-time https://arxiv.org/abs/0805.2417 ...

- Peres https://arxiv.org/abs/quant-ph/0310010

- Fuchs https://www.quantamagazine.org/quantum-bayesianism-explained-by-its-founder-20150604/

- Rovelli https://arxiv.org/abs/quant-ph/0604064

- Jaynes https://bayes.wustl.edu/etj/articles/cmystery.pdf (1)...

When only Alice carries out measurements on her side, the prediction of the conservation of the 2-time correlation on Bob's side, resulting from the 2-state vector time-symmetric formalism, decides, in my view, in favor of the positivist interpretation of the EPR non-locality. In my view, the positivist interpretation becomes a falsifiable physical postulate instead of a pure philosophical question.

Such an experimental verification seems solving a 40 years debate between positivist and realist interpretations of Bell's inequalities violation. Hence, this experimental validation seems deserving to be carried out (but I don't know if it has already been Achieved).

Would you agree with this view?

(1) Note, however, that E.T. Jaynes supports a realist interpretation of physics and its role despite, paradoxically, his insistence on the importance of Bayesian inference and the broad development he gave to this approach (cf. Maxent https://bayes.wustl.edu/etj/articles/rational.pdf)

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